Music services are gaining in popularity as more consumers seek immediate access to comprehensive music libraries. For example, instead of purchasing individual copies of songs or albums (e.g., CDs), consumers can register with various music services to access music offered by the music services. Particularly, various music services and platforms enable users to download or stream specific songs, albums, or playlists to desktop applications or mobile devices. One benefit advertised by many music services is the ability to discover new music and/or music that could be of potential interest to the users. However, there are deficiencies in existing music services and platforms.
In particular, many existing music services utilize a user's listening history or song library to compile suggestions for new or potentially interesting music, including songs, albums, or playlists. However, a user that is new to the music service may not have a history or library from which to build suggestions. As a result, the music suggestions may not be very effective or meaningful until the user creates a substantial listening history or music library with the music service. The longer it takes for the music service to reach the requisite level of effectiveness, the more likely a user will become frustrated and discontinue use of the music service.
Accordingly, with the advent and popularity of music services offering users the ability to expand their access to music libraries, there is an opportunity for implementing systems and methods for quickly and accurately determining the personal preferences of a user, particularly during an initial use of a music service.